virenk / coursera-johnhopkins-ds-ml Goto Github PK

 @ Author: Virendra Kumar
 @ Date:   17/12/2014
 @Contact:  [email protected]

#This project is done for the oursera course Practical Machine Learning, John Hopkins University in December, 2014.

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Background: 
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Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, your goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available from the website here: http://groupware.les.inf.puc-rio.br/har (see the section on the Weight Lifting Exercise Dataset).

Data:
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The training data for this project are available here: 
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv

The test data are available here: 
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv

The data for this project come from this source: http://groupware.les.inf.puc-rio.br/har. If you use the document you create for this class for any purpose please cite them as they have been very generous in allowing their data to be used for this kind of assignment.


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The data from the file pml-training.csv is used for the training and in Sample testing of the model. 
First those variables which have more than 50% NA's in the data are dropped,  and then those variables which doesn't make any significance for the model are dropped from the data.

Then this data is divided into two parts, 80% for training and 20% testing data using createDataPartition function from the caret library.

Now the model is trained using randomForest technique for the training data set.

randomForest: 
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randomForest implements Breiman's random forest algorithm (based on Breiman and Cutler's original Fortran code) for classification and regression. It can also be used in unsupervised mode for assessing proximities among data points.

randomForest(x, y=NULL,  xtest=NULL, ytest=NULL, ntree=500,
             mtry=if (!is.null(y) && !is.factor(y))
             max(floor(ncol(x)/3), 1) else floor(sqrt(ncol(x))),
             replace=TRUE, classwt=NULL, cutoff, strata,
             sampsize = if (replace) nrow(x) else ceiling(.632*nrow(x)),
             nodesize = if (!is.null(y) && !is.factor(y)) 5 else 1,
             maxnodes = NULL,
             importance=FALSE, localImp=FALSE, nPerm=1,
             proximity, oob.prox=proximity,
             norm.votes=TRUE, do.trace=FALSE,
             keep.forest=!is.null(y) && is.null(xtest), corr.bias=FALSE,
             keep.inbag=FALSE, ...)

# By default 500 trees are trained for this random forest model.